Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Gao, Mingchen; Bağcı, Ulaş; Lü, Le; Wu, Aaron; Buty, Mario; Shin, Hoo-Chang; Roth, Holger R.; Papadakis, Georgios Z.; Depeursinge, Adrien; Summers, Ronald M.; Xu, Ziyue; Mollura, Daniel J.

doi:10.1080/21681163.2015.1124249

Cited by 221 publications

(180 citation statements)

References 15 publications

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“…Interstitial lung disease Anthimopoulos et al (2016) Classification of 2D patches into interstitial lung texture classes using a standard CNN Christodoulidis et al (2017) 2D interstitial pattern classification with CNNs pre-trained with a variety of texture data sets Gao et al (2016c) Propagates manually drawn segmentations using CNN and CRF for more accurate interstitial lung disease reference Gao et al (2016a) AlexNet applied to large parts of 2D CT slices to detect presence of interstitial patterns Gao et al (2016b) Uses regression to predict area covered in 2D slice with a particular interstitial pattern Tarando et al (2016) Combines existing computer-aided diagnosis system and CNN to classify lung texture patterns. van Tulder and de Bruijne (2016) Classification of lung texture and airways using an optimal set of filters derived from DBNs and RBMs Other applications Tajbakhsh et al (2015a) Multi-stream CNN to detect pulmonary embolism from candidates obtained from a tobogganing algorithm Carneiro et al (2016) Predicts 5-year mortality from thick slice CT scans and segmentation masks de Vos et al (2016a) Identifies the slice of interest and determine the distance between CT slices…”

Section: Eyementioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

9,665

5,414

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Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

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Section: Eyementioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

9,665

5,414

View full text Add to dashboard Cite

show abstract

“…To address the problems described above, in this work we propose a method to compute strain in the line of action of tongue muscles with the aid of 4D motion atlases [14,15] constructed from normal subjects. Since atlases of the tongue have been a useful tool in analyzing motion in subject-specific spaces [16] , they can be applied in various scenarios to provide missing information in a statistical way.…”

Section: Introductionmentioning

confidence: 99%

Strain map of the tongue in normal and ALS speech patterns from tagged and diffusion MRI

Xing

Prince

Stone

et al. 2018

Medical Imaging 2018: Image Processing

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Amyotrophic Lateral Sclerosis (ALS) is a neurological disease that causes death of neurons controlling muscle movements. Loss of speech and swallowing functions is a major impact due to degeneration of the tongue muscles. In speech studies using magnetic resonance (MR) techniques, diffusion tensor imaging (DTI) is used to capture internal tongue muscle fiber structures in three-dimensions (3D) in a non-invasive manner. Tagged magnetic resonance images (tMRI) are used to record tongue motion during speech. In this work, we aim to combine information obtained with both MR imaging techniques to compare the functionality characteristics of the tongue between normal and ALS subjects. We first extracted 3D motion of the tongue using tMRI from fourteen normal subjects in speech. The estimated motion sequences were then warped using diffeomorphic registration into the b0 spaces of the DTI data of two normal subjects and an ALS patient. We then constructed motion atlases by averaging all warped motion fields in each b0 space, and computed strain in the line of action along the muscle fiber directions provided by tractography. Strain in line with the fiber directions provides a quantitative map of the potential active region of the tongue during speech. Comparison between normal and ALS subjects explores the changing volume of compressing tongue tissues in speech facing the situation of muscle degradation. The proposed framework provides for the first time a dynamic map of contracting fibers in ALS speech patterns, and has the potential to provide more insight into the detrimental effects of ALS on speech.

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“…From an application perspective, there are various literature reports that applied CNN models to analyze medical images with lung diseases [15,16,17,18] and obtained encouraging results. As we discussed previously, the lack of training data is especially challenging in medical image analysis.…”

Section: Related Workmentioning

confidence: 99%

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

Zhong

Lin

et al. 2017

Machine Learning in Medical Imaging

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Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning to medical image analysis. However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples. Specifically, annotation and labeling of the medical images is much more expensive and time-consuming than other applications and often involves manual labor from multiple domain experts. In this work, we propose a multi-stage, self-paced learning framework utilizing a convolutional neural network (CNN) to classify Computed Tomography (CT) image patches. The key contribution of this approach is that we augment the size of training samples by refining the unlabeled instances with a self-paced learning CNN. By implementing the framework on high performance computing servers including the NVIDIA DGX1 machine, we obtained the experimental result, showing that the self-pace boosted network consistently outperformed the original network even with very scarce manual labels. The performance gain indicates that applications with limited training samples such as medical image analysis can benefit from using the proposed framework.

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Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Cited by 221 publications

References 15 publications

A survey on deep learning in medical image analysis

A survey on deep learning in medical image analysis

Strain map of the tongue in normal and ALS speech patterns from tagged and diffusion MRI

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

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